Using R for time series analysis and spatial-temporal distribution of global burnt surface multi-year product
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1 Using R for time series analysis and spatial-temporal distribution of global burnt surface multi-year product Jedrzej Bojanowski* César Carmona-Moreno European Commission - Joint Research Centre Institute for Environment and Sustainability Global Environment Monitoring Unit, Ispra, Italy * Institute of Geodesy and Cartography, Remote Sensing Department, Warsaw, Poland The R User Conference 2008, August Technische Universitt Dortmund, Germany
2 Problem R vs GIS Methods Further works
3 The need for global burnt area product Fires: a significant component of global ecosystem Influence on climate, carbon cycle, pollution... Climate change?
4 The need for global burnt area product Fires: a significant component of global ecosystem Influence on climate, carbon cycle, pollution... Climate change? PROBLEM Lack of an exhaustive base of past fires activities!
5 The need for global burnt area product Fires: a significant component of global ecosystem Influence on climate, carbon cycle, pollution... Climate change? PROBLEM Lack of an exhaustive base of past fires activities! TO DO Concatenation of two existing databases: GBS and L 3 JRC
6 GBS and L 3 JRC GBS L 3 JRC time range input data NOAA/AVHRR SPOT VEGETATION temporal resolution 1 week 1 day spatial resolution approx 8 km approx 1 km advantages seasonality! area estimates!
7 Why R and not GIS? Wide functionality Import of all data formats Easy data manipulation Statistical and geostatistical analysis Graph plotting Map plotting Results into LaTeX code
8 Why R and not GIS? Wide functionality Import of all data formats Easy data manipulation Statistical and geostatistical analysis Graph plotting Map plotting Results into LaTeX code AUTOMATION!
9 Methods Data import Data manipulation Time series analysis Regression modeling Principal components analysis & 3D visualization Spatial temporal distribution visualization technique
10 Data import and storage GeoTiff rgdal rnetcdf data manipulation NetCDF Analyses GeoTiff Vizualization
11 Generalization GBS 8 km weekly L3JRC 1 km daily GENERALIZATION 0.5 degree monthly
12 Time series
13 Seasonality shift Time series for Kazahstan Burnt area in [ km 2 ] Time Area burnt in months in GBS Area burnt in months in L3JRC Burnt area in [ km 2 ] Months Months
14 Area estimation latitude (GBS) (L3JRC) Yearly mean burnt area [ km 2 ]
15 Probability map
16 Probability extension algorithm
17 Probability extension algorithm
18 Probability extension algorithm
19 Probability extension algorithm
20 Probability extension algorithm
21 Probability extension algorithm
22 Probability extension algorithm
23 Probability map after extension
24 Principal Components & 3D interactive visualization
25 Principal Components & 3D interactive visualization
26 Spatial-temporal distribution
27 Conclusion Probability extension algorithm PCA with 3D interactive visualization Map of spatial-temporal distribution of global data
28 Conclusion Probability extension algorithm PCA with 3D interactive visualization Map of spatial-temporal distribution of global data rgdal data import RNetCDF data storage zoo time series analysis rgl 3D interactive plots spatial interpolation PET image rotation fields raster maps plotting
29 THANK YOU FOR YOUR ATTENTION!
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